The high complexity of aquatic ecosystems and the multiple processes involved, make the development of ecohydraulics and eco-environmental models a challenging subject. Conventionally, computer-based models use a mathematical formulation for the processes involved which are then solved by numerical methods. These models are often derived based on the assumption of spatial homogeneity and conservation principles of mass, momentum and energy. Development of these models often demands a clear understanding of the processes involved. However, the above assumptions are easily violated when spatial heterogeneity, individual species behaviour and local interactions play a significant role in the system dynamics. In particular for eco-environmental systems, knowledge on local interactions that determine the overall system behaviour is not always available. Although the rapid advances of data-driven techniques have recently made great contributions to water-environment related research, data on ecosystems are often quite limited, which restricts the application of data mining methods to eco-environmental system modelling. In addition, no modelling – also not black-box modelling – can be undertaken without having at least some understanding of the basic processes and mechanisms involved. It is always advisable to start exploring any dataset using conventional statistical techniques, as elaborated in this paper for a case study on Western Xiamen Bay, China. Neural network trimming was then used to establish the dominant factors; it was shown that a relatively simple ANN model was quite capable of capturing the essential features, provided the right input parameters are chosen. Examples of integrated approaches to ecohydraulics modelling coupling formulations with cellular automata and physical equations with fuzzy rules are presented for applications on eutrophication modelling of Taihu Lake in China, competitive growths and colonization of two underwater macrophytes in Lake Veluwe in The Netherlands, and forecasting of algal blooms in the Dutch coastal waters on the North Sea. A mussel dynamics model developed for the Upper Mississippi River in the USA demonstrates the feasibility of individual based modelling in ecosystem dynamics. Numerical models are quite capable of simulating the abiotic aquatic environment, including complicated fluid flow and transport mechanisms. However, when it comes to simulating the biotic and ecosystem dynamics, the interaction of individual species with their environment, as well as the interactions amongst species, has to be taken into account. The future of ecohydraulics and eco-environmental modelling thus seems to lie in the integration of different paradigms and techniques, which is the core content of the hydroinformatics discipline.